The advent of global communications networks such as the Internet has presented commercial opportunities for reaching vast numbers of potential customers. Electronic messaging, and particularly electronic mail (“e-mail”), is becoming increasingly pervasive as a means for disseminating unwanted advertisements and promotions (also denoted as “spam”) to network users.
The Radicati Group, Inc., a consulting and market research firm, estimates that as of August 2002, two billion junk e-mail messages are sent each day—this number is expected to triple every two years. Individuals and entities (e.g., businesses, government agencies, . . . ) are becoming increasingly inconvenienced and oftentimes offended by junk messages. As such, junk e-mail is now or soon will become a major threat to trustworthy computing.
A key technique utilized to thwart junk e-mail is employment of filtering systems/methodologies. One proven filtering technique is based upon a machine learning approach—machine learning filters assign to an incoming message a probability that the message is junk. In this approach, features typically are extracted from two classes of example messages (e.g., junk and non-junk messages), and a learning filter is applied to discriminate probabilistically amongst the two classes. Since many message features are related to content (e.g., words and phrases in the subject and/or body of the message), such types of filters are commonly referred to as “content-based filters”.
Some junk/spam filters are adaptive, which is important in that multilingual users and users who speak rare languages need a filter that can adapt to their specific needs. Furthermore, not all users agree on what is and is not, junk/spam. Accordingly, by employing a filter that can be trained implicitly (e.g., via observing user behavior) the respective filter can be tailored dynamically to meet a user's particular message identification needs.
One approach for filtering adaptation is to request a user(s) to label messages as junk and non-junk. Unfortunately, such manually intensive training techniques are undesirable to many users due to the complexity associated with such training let alone the amount of time required to properly effect such training. Another adaptive filter training approach is to employ implicit training cues. For example, if the user(s) replies to or forwards a message, the approach assumes the message to be non-junk. However, using only message cues of this sort introduces statistical biases into the training process, resulting in filters of lower respective accuracy.
Still another approach is to utilize all user(s) e-mail for training, where initial labels are assigned by an existing filter and the user(s) sometimes overrides those assignments with explicit cues (e.g., a “user-correction” method)—for example, selecting options such as “delete as junk” and “not junk”—and/or implicit cues. Although such an approach is better than the techniques discussed prior thereto, it is still deficient as compared to the subject invention described and claimed below.